文件名称：python Algorithms 2nd Edition
文件大小：4.78MB
文件格式：PDF
更新时间：2020-12-18 13:04:36
Python 算法
Consider the following problem: You are to visit all the cities, towns, and villages of, say, Sweden and then return to your starting point. This might take a while (there are 24,978 locations to visit, after all), so you want to minimize your route. You plan on visiting each location exactly once, following the shortest route possible. As a programmer, you certainly don’t want to plot the route by hand. Rather, you try to write some code that will plan your trip for you. For some reason, however, you can’t seem to get it right. A straightforward program works well for a smaller number of towns and cities but seems to run forever on the actual problem, and improving the program turns out to be surprisingly hard. How come? Actually, in 2004, a team of five researchers1 found such a tour of Sweden, after a number of other research teams had tried and failed. The five-man team used cutting-edge software with lots of clever optimizations and tricks of the trade, running on a cluster of 96 Xeon 2.6GHz workstations. Their software ran from March 2003 until May 2004, before it finally printed out the optimal solution. Taking various interruptions into account, the team estimated that the total CPU time spent was about 85 years! Consider a similar problem: You want to get from Kashgar, in the westernmost region of China, to Ningbo, on the east coast, following the shortest route possible.2 Now, China has 3,583,715 km of roadways and 77,834 km of railways, with millions of intersections to consider and a virtually unfathomable number of possible routes to follow. It might seem that this problem is related to the previous one, yet this shortest path problem is one solved routinely, with no appreciable delay, by GPS software and online map services. If you give those two cities to your favorite map service, you should get the shortest route in mere moments. What’s going on here?